Credit Scoring Models by AI Companies: A Comprehensive Guide

Banks exist to make credit scoring decisions. Key function of banks is to enable individuals and companies to make expenditures before they can afford them. They can not accomplish this key function without becoming a competent estimator of who will pay back and who will default.

Banks always relied on models and experts to make effective credit decisions. Now models are becoming sophisticated enough to replace experts. AI companies are stepping in to provide these models to banks so they can focus on serving their customers.

What value do AI companies bring to credit scoring?

When it comes to credit scoring, banks already hold the most valuable data: repayments. Lona repayment track records are extremely valuable in understanding market dynamics and banks are the only institutions that hold granular data on repayments. They would need to share anonymized data on repayments with AI companies so they can build effective models.

While they lack unique data, AI companies bring model building expertise and AI talent. It is expensive to develop an in-house data science/AI team as a bank since

Tech enthusiasts find it less enticing to work in industries traditionally not considered tech

Data science talent is scarce and expensive

It takes time and effort to gather data other than loan repayment data. However granular data on topics such as the applicant’s income, career, education or expenditures can be quite valuable in credit scoring

Is there regulatory challenges to using diverse sets of data for credit scoring?

Currently, no. At least in United States. B2C loan provider fintech company Upstart applied to The Consumer Financial Protection Bureau to get an opinion on its use of alternative data in loan applications and received a “no-action letter” allowing the startup to continue its use of alternative data.

We examined top AI companies providing credit scoring models to banks and other financial institutions.

AppliedAI

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